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Estimating demand for opera using sales system data: the case of Finnish National Opera

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  • Jani-Petri Laamanen

Abstract

Using detailed data for 2001–2009 from the sales system of the Finnish National Opera, we estimate the determinants of demand for opera tickets. We find that operas in their premiere season are more popular than reprises. Demand is lower for classical operas and higher for domestic operas and for performances with a famous opera singer. Press reviews and the overall popularity of the opera piece have the expected effects. There is also evidence of seasonal effects. By excluding temporarily discounted tickets, controlling for performance characteristics and quality and using a method that takes into account capacity constraints, we are able to credibly estimate the price elasticity of demand. The overall elasticity is close to unity: on average, a 1 % increase in prices would result in 1.16 % decrease in demand. Copyright Springer Science+Business Media New York 2013

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  • Jani-Petri Laamanen, 2013. "Estimating demand for opera using sales system data: the case of Finnish National Opera," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(4), pages 417-432, November.
    • Handle: RePEc:kap:jculte:v:37:y:2013:i:4:p:417-432
    DOI: 10.1007/s10824-012-9190-6
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    Cited by:

    1. Ozhegova, A. & Ozhegov, E., 2018. "Estimation of Demand Function for Performing Arts: Empirical Analysis," Journal of the New Economic Association, New Economic Association, vol. 37(1), pages 87-110.
    2. Alina Ozhegova & Evgeniy M. Ozhegov, 2018. "Heterogeneity in demand for performances and seats in the theatre," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 131-145, June.
    3. Kirstin Hallmann & Cristina Muñiz Artime & Christoph Breuer & Sören Dallmeyer & Magnus Metz, 2017. "Leisure participation: modelling the decision to engage in sports and culture," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(4), pages 467-487, November.
    4. Andrea Baldin & Trine Bille, 2018. "Modelling preference heterogeneity for theatre tickets: a discrete choice modelling approach on Royal Danish Theatre booking data," Applied Economics, Taylor & Francis Journals, vol. 50(5), pages 545-558, January.
    5. Eric Kolhede & J. Tomas Gomez-Arias & Anna Maximova, 2023. "Price elasticity in the performing arts: a segmentation approach," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 523-550, September.
    6. Ozhegova, Alina & Ozhegov, Evgeniy M., 2020. "Segmentation of theatre audiences: A latent class approach for combined data," Journal of choice modelling, Elsevier, vol. 37(C).
    7. Junlong Wu & Keshen Jiang & Chaoqing Yuan, 2019. "Determinants of demand for traditional Chinese opera," Empirical Economics, Springer, vol. 57(6), pages 2129-2148, December.
    8. Schlosser, Rainer, 2017. "Stochastic dynamic pricing and advertising in isoelastic oligopoly models," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1144-1155.
    9. Evgeniy M. Ozhegov & Alina Ozhegova, 2018. "Segmentation of Theatre Audiences: A Latent Class Approach for Combined Data," HSE Working papers WP BRP 198/EC/2018, National Research University Higher School of Economics.
    10. Alina R. Buzanakova & Evgeniy M. Ozhegov, 2016. "Demand for Performing Arts: The Effect of Unobserved Quality on Price Elasticity," HSE Working papers WP BRP 156/EC/2016, National Research University Higher School of Economics.
    11. Andrea Baldin & Trine Bille & Andrea Ellero & Daniela Favaretto, 2018. "Revenue and attendance simultaneous optimization in performing arts organizations," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 42(4), pages 677-700, November.
    12. Marta Zieba, 2017. "Cultural participation of tourists – Evidence from travel habits of Austrian residents," Tourism Economics, , vol. 23(2), pages 295-315, March.
    13. John S. Jatta & Krishna Kumar Krishnan, 2016. "An empirical assessment of a univariate time series for demand planning in a demand-driven supply chain," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(3), pages 269-290.
    14. Tzu-Ming Liu, 2020. "Habit formation or word of mouth: What does lagged dependent variable in tourism demand models imply?," Tourism Economics, , vol. 26(3), pages 461-474, May.
    15. Andrea Baldin & Trine Bille & Andrea Ellero & Daniela Favaretto, 2016. "Multiobjective optimization model for pricing and seat allocation problem in non profit performing arts organization," Working Papers 20, Department of Management, Università Ca' Foscari Venezia.

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    More about this item

    Keywords

    Demand estimation; Opera; Performing arts; Z11; L32; L82;
    All these keywords.

    JEL classification:

    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature
    • L32 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Public Enterprises; Public-Private Enterprises
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

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